
CONCLUSTION
This paper presents a hidden semi-Markov models based
framework for leveraging large scale digital history coded
events captured from GDELT to utilize the frequent subgraph
patterns mined from the GDELT event streams to uncover
the underlying event evolution mechanics and formulate the
social unrest event prediction as a sequence classification
problem. Extensive empirical testing with data from Thailand
in the Southeast Asia demonstrated the effectiveness
of
this
framework by comparing it with traditional HMM, the logistic
regression model and the baseline model.
It
shows that the
GDELT dataset
do
reflect some useful precursor indicators
that reveal the causes or evolution
of
future events.
We
plan to conduct our future work in the following three
aspects. First, we plan to introduce a multi-level prediction
mechanism to our framework, such
as
city level or province
level. Second, in GDELT 2.0, event mention details and
global knowledge graphs
[32]
are also provided real-timely,
which can bring
us
with detail insights to the events. More
machine learning and deep learning methods like the graph
neural networks [33] can be developed with more events'
elements. Third, the prediction framework may be improved
by distinguishing widespread news coverage from localized
coverage.
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